Advanced metering infrastructure for low voltage distribution system in smart grid based monitoring applications

Pandraju, Thandava Krishna Sai; Samal, Sarita; Saravanakumar, R.; Yaseen, Syed Mufassir; Nandal, Rainu; Dhabliya, Dharmesh

doi:10.1016/j.suscom.2022.100691

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…One of the major achievements of smart grids was the development of the Advanced Metering Infrastructure (AMI) system [1]. This system reduces the danger associated with electricity theft by using its fine-grained computations and tracing ability [2].…”

Section: Introductionmentioning

confidence: 99%

Detecting Nontechnical Losses in Smart Meters Using a MLP-GRU Deep Model and Augmenting Data via Theft Attacks

et al. 2022

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The current study uses a data-driven method for Nontechnical Loss (NTL) detection using smart meter data. Data augmentation is performed using six distinct theft attacks on benign users’ samples to balance the data from honest and theft samples. The theft attacks help to generate synthetic patterns that mimic real-world electricity theft patterns. Moreover, we propose a hybrid model including the Multi-Layer Perceptron and Gated Recurrent Unit (MLP-GRU) networks for detecting electricity theft. In the model, the MLP network examines the auxiliary data to analyze nonmalicious factors in daily consumption data, whereas the GRU network uses smart meter data acquired from the Pakistan Residential Electricity Consumption (PRECON) dataset as the input. Additionally, a random search algorithm is used for tuning the hyperparameters of the proposed deep learning model. In the simulations, the proposed model is compared with the MLP-Long Term Short Memory (LSTM) scheme and other traditional schemes. The results show that the proposed model has scores of 0.93 and 0.96 for the area under the precision–recall curve and the area under the receiver operating characteristic curve, respectively. The precision–recall curve and the area under the receiver operating characteristic curve scores for the MLP-LSTM are 0.93 and 0.89, respectively.

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Section: Introductionmentioning

confidence: 99%

Detecting Nontechnical Losses in Smart Meters Using a MLP-GRU Deep Model and Augmenting Data via Theft Attacks

et al. 2022

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Section: Introductionmentioning

confidence: 99%

“…Transmission systems and medium voltage distribution networks are frequently well equipped with advanced monitoring devices for control and planning purposes [1]. In contrast, low voltage (LV) distribution networks have been operated with very few measurements to compute the power flow driven by local end users [1]. The lack of observability in LV distribution networks combined with the increasing deployment of distributed energy resources (DER) connected on the customer side has negatively impacted the stability, flexibility, and resilience of distribution networks [1].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, low voltage (LV) distribution networks have been operated with very few measurements to compute the power flow driven by local end users [1]. The lack of observability in LV distribution networks combined with the increasing deployment of distributed energy resources (DER) connected on the customer side has negatively impacted the stability, flexibility, and resilience of distribution networks [1]. The increasing availability of advanced metering infrastructure (e.g., smart meters), which in some European countries has reached over 80% rollout [2], has however started to enable a further observability in secondary LV networks.…”

Section: Introductionmentioning

confidence: 99%

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Impact Assessment of NILM Methods for an Enhanced Observability of Low Voltage Distribution Networks

Jaramillo

Lopez‐Lorente

Brogan³

et al. 2022

2022 IEEE International Smart Cities Conference (ISC2)

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Assessment and classification of grid stability with cost-sensitive stacked ensemble classifier

Ramasamy¹,

Arivoli²,

Velusamy³

2023

Automatika

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Advanced metering infrastructure for low voltage distribution system in smart grid based monitoring applications

Cited by 8 publications

References 16 publications

Detecting Nontechnical Losses in Smart Meters Using a MLP-GRU Deep Model and Augmenting Data via Theft Attacks

Detecting Nontechnical Losses in Smart Meters Using a MLP-GRU Deep Model and Augmenting Data via Theft Attacks

Impact Assessment of NILM Methods for an Enhanced Observability of Low Voltage Distribution Networks

Assessment and classification of grid stability with cost-sensitive stacked ensemble classifier

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